Abstract
Background
Advanced biological aging is associated with an increased risk of adverse health effects. However, the association between biological aging acceleration (BAA) and kidney stone (KS) disease remains unclear. This study aimed to examine the association between BAA and KS and to assess whether insulin resistance (IR) mediates this association.
Methods
A total of 10,227 non-pregnant adults (mean age 47.96 years, 50.03% male) from the National Health and Nutrition Examination Survey (NHANES) 2007–2018 cycles were included in this cross-sectional study. Biological age was measured using the Klemera-Doubal method biological age (KDM-BA) and phenotypic age (PhenoAge) algorithms, and BAA was quantified by residual analysis and compared with chronological age. IR was assessed using five surrogates: the triglyceride-glucose (TyG) index, TyG-waist circumference (TyG-WC), TyG-waist-to-height ratio (TyG-WHtR), TyG-body mass index (TyG-BMI), and the metabolic score for insulin resistance (METS-IR). The relationships between BAA, IR surrogates, and KS were investigated using multivariable logistic regression models. To find out if IR mediates the association between BAA and KS, a mediation study was conducted.
Results
After adjusting for all covariates, adults in the highest KDM-BA acceleration tertile had a 21% [odds ratio (OR): 1.21, 95% confidence interval (CI): 1.02–1.44] increased risk of KS compared with those in the lowest tertile. The multivariable-adjusted OR for KS comparing participants in the extreme PhenoAge acceleration tertiles was 1.77 (95% CI: 1.47–2.13). Mediation analyses indicated that the five IR surrogates statistically mediated part of the association between PhenoAge acceleration and KS, with the mediated proportions ranging from 1.67% to 29.34% (all P<0.001). No significant mediating effects were observed in the association between KDM-BA acceleration and KS risk.
Conclusions
In this cross sectional analysis, an elevated risk of KS is significantly associated with accelerated biological aging, and this association is partially explained by IR. These findings may help develop targeted prevention strategies for KS in aging populations.
Keywords: Kidney stone (KS), biological aging acceleration (BAA), triglyceride-glucose index (TyG index), insulin resistance (IR)
Highlight box.
Key findings
• Accelerated biological aging, measured by Klemera-Doubal method biological age (KDM-BA) and phenotypic age (PhenoAge) algorithms, is significantly associated with a higher prevalence of kidney stones in a large, nationally representative adult population.
• Insulin resistance (IR; assessed via five surrogate indices) statistically mediates a portion (1.67% to 29.34%) of the association between PhenoAge acceleration and kidney stones.
What is known and what is new?
• Biological aging increases susceptibility to chronic diseases, and IR is a known risk factor for kidney stone formation.
• This is the first study to demonstrate a significant association between accelerated biological aging and kidney stone prevalence and to identify IR as a potential explanatory pathway in this relationship.
What is the implication, and what should change now?
• These findings suggest that biological aging and associated metabolic dysfunction, particularly IR, should be considered in kidney stone risk assessment.
• Future research should explore whether interventions targeting metabolic health and aging-related pathways could offer novel strategies for kidney stone prevention.
Introduction
Kidney stone (KS) disease is a prevalent and clinically significant condition with substantial implications for the healthcare systems globally (1,2). Its high recurrence rate and potential complications, such as infections, obstruction, and renal impairment, and the resulting detrimental effects on patients’ quality of life make it a major public health concern (3). Numerous studies have examined various metabolic and environmental determinants of KS, including factors such as obesity, dietary habits, urinary chemistry, hydration status, and environmental, geographical, and climatic conditions, such as temperature, altitude, and regional dietary patterns (4,5).
Biological aging encompasses a series of physiological changes that affect the structure and function of organs, tissues, and cells over time (6). It is influenced by a complex interplay of genetic, environmental, and lifestyle factors, ultimately leading to a decline in biological function and an increased vulnerability to various diseases (6,7). This process increases susceptibility to various chronic diseases, such as cardiovascular disorders, diabetes, and neurodegenerative diseases (8-11). However, the association between biological aging and the prevalence of KS remains unclear.
The mechanisms of biological aging are multifaceted and encompass a range of cellular, molecular, and physiological processes (12,13). These include accumulation of cellular damage, mitochondrial dysfunction, telomere shortening, and alterations in immune function (14-17). One key mechanism involved in biological aging is insulin resistance (IR), which plays a central role in the pathophysiology of many age-related diseases (18-20). IR is also closely associated with an increased risk of KS formation (21,22). However, the role of IR in the relationship between biological aging and KS disease remains unclear. Given that IR is associated with both biological aging and increased KS formation, we postulated that the association between the prevalence of KS and biological aging might be mediated by IR.
To address these knowledge gaps, we conducted a crosssectional analysis using data from the National Health and Nutrition Examination Survey (NHANES). We aimed to investigate the association between accelerated biological aging—quantified by the Klemera-Doubal method biological age (KDM-BA) and phenotypic age (PhenoAge)—and KS prevalence (23). Furthermore, no prior study has examined whether IR mediates this association in a large, population-based sample; we therefore sought to evaluate this potential pathway. We present this article in accordance with the STROBE reporting checklist (available at https://tau.amegroups.com/article/view/10.21037/tau-2025-741/rc).
Methods
Study population
The NHANES provides critical biomonitoring data on the health and nutritional status of the U.S. civilian population through standardized examinations of a nationally representative cohort. As a cross-sectional survey, NHANES collects data at a single point in time, enabling the analysis of health parameters and exposure-disease relationships across the population without longitudinal follow-up. This foundational dataset, comprised of objective biomedical measures derived from physical examinations, laboratory assays, and diagnostic tests, is essential for identifying disease risk factors, establishing population reference ranges, and informing evidence-based public health policies. The NHANES protocol was approved by the National Center for Health Statistics Research Ethics Review Board. All participants provided written informed consent. This secondary analysis of de-identified public data was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.
Data for this analysis were obtained from 32,016 non-pregnant adult participants (aged 20–80 years) in the NHANES between 2007 and 2018. The exclusion criteria were 78 individuals without information on KS and 5,020 individuals with missing biological age data, leaving 26,918 participants. In addition, 16,691 participants with incomplete covariate or IR surrogate data were excluded. Ultimately, the final study population consisted of 10,227 non-pregnant adults, and a flowchart of the eligibility criteria is provided in Figure S1.
Assessment of biological ages and age accelerations
KDM-BA and PhenoAge algorithm, which were computed with R package “BioAge”, were used to ascertain participant biological ages in the current study.
The KDM-BA, computed via the BioAge package, represents an individual’s biological age based on the physiological state at which their biomarkers are normative, as defined by regression models trained on the NHANES III reference population (non-pregnant adults aged 30–75 years) (24). The KDM-BA formula has been described elsewhere (25). The biomarker panel for the original KDM-BA algorithm comprised nine measures: forced expiratory volume in one second (FEV1), systolic blood pressure, albumin, alkaline phosphatase, blood urea nitrogen, creatinine, C-reactive protein (CRP), glycated hemoglobin, and total cholesterol. In our study, we used seven biomarkers: systolic blood pressure, albumin, alkaline phosphatase, blood urea nitrogen, creatinine, glycated hemoglobin, and total cholesterol (Table S1). CRP was not included in the algorithm because it was not measured in the 2011–2012 and 2013–2014 survey cycles.
PhenoAge, a widely-used measure of biological age, represents the chronological age at which an individual’s mortality risk is average in a reference population. It was calculated using chronological age and a panel of eight biomarkers: albumin, alkaline phosphatase, creatinine, glucose, mean cell volume, red cell distribution width, white blood cell count, and lymphocyte proportion (26) (Table S1).
To measure biological aging, we regressed the computed biological age values on chronological age and computed the residual values, which were termed “biological age acceleration” values. We further standardized the biological aging acceleration (BAA) values to have a mean of 0 and standard deviation (SD) of 1 for analyses (27).
Definitions of KSs
The answers to the question “Have you/has sample person (SP) ever had kidney stones?” were used to determine KS. KS was diagnosed in participants who responded in the affirmative to the aforementioned question.
Assessment of IR
Fasting plasma specimens were collected to measure fasting glucose, triglyceride, and high-density lipoprotein cholesterol (HDL-C) levels. During physical examinations at a mobile examination center, participants’ height, waist circumference, and body weight were measured. Weight (kg) divided by height (m) squared was used to compute body mass index (BMI). The following IR surrogates were calculated: triglyceride-glucose (TyG) index: Ln[fasting triglyceride (mg/dL) × fasting glucose (mg/dL)]/2; TyG-waist circumference (TyG-WC): TyG × waist circumference (cm); TyG-waist-to-height ratio (TyG-WHtR): TyG × [waist circumference (cm) / height (cm)]; TyG-body mass index (TyG-BMI): TyG × BMI (kg/m2) (28). Metabolic score for insulin resistance (METS-IR): Ln{[2 × fasting glucose (mg/dL)] + fasting triglyceride (mg/dL)} × BMI (kg/m2)/Ln[HDL-C (mg/dL)] (29).
Covariates
Data were gathered on key participant characteristics through structured questionnaires conducted by trained staff. The information encompassed demographic profiles (age, sex, race/ethnicity), socioeconomic indicators (education level, household income), and behavioral factors (smoking and drinking status). During the clinical examination, blood pressure readings were obtained using mercury sphygmomanometers. For outcome ascertainment, we applied standardized diagnostic criteria: Hypertension was identified by elevated systolic (≥140 mmHg) or diastolic (≥90 mmHg) pressure, a clinical history of diagnosis, or active antihypertensive medication use. Diabetes was confirmed based on elevated fasting glucose (≥126 mg/dL), elevated hemoglobin A1c (HbA1c) (≥6.5%), a positive oral glucose tolerance test (OGTT) result (2-h glucose ≥200 mg/dL), previous diagnosis, or the use of insulin/oral antidiabetic drugs. Hyperuricemia was characterized by serum uric acid levels exceeding 7.0 mg/dL in males and 6.0 mg/dL in females.
Statistical analysis
The distribution of continuous variables was evaluated for normality via quantile-quantile (Q-Q) plots. Descriptive statistics are reported as mean ± SD for continuous data and as counts (percentages) for categorical data. To analyze the population by biological aging pace, subjects were divided into tertiles of BAA. To compare baseline characteristics across these tertiles, one-way analysis of variance (ANOVA) was applied to continuous variables, while chi-square or Fisher’s exact test was used for categorical variables. Finally, the association between participant characteristics and BAA was quantified by calculating the mean (SD) of BAA for each subgroup, with comparisons conducted via Student’s t-tests.
Multivariable logistic regression was used to evaluate the association between the odds of KS and BAA. Two models were built: basic demographics, such as age (continuous), sex, and race/ethnicity, were taken into account while adjusting Model 1. Socioeconomic and lifestyle-confounding variables, such as educational attainment, family poverty-income ratio (PIR), current drinking and smoking status, physical activity level, and the presence or absence of hypertension, diabetes, and hyperuricemia, were further accounted for in Model 2. Furthermore, 95% confidence intervals (CIs) were used to calculate the odds ratio (OR) for KS for every SD rise in BAA. Using restricted cubic splines (RCS) with four knots at the 5th, 35th, 65th, and 95th percentiles, the possible nonlinear dose-response relationship was shown. Stratified analysis and multiplicative interaction tests were performed to evaluate effect modification. The relative excess risk due to interaction (RERI) was computed in order to further quantify the additive interaction.
Sensitivity analyses were used to assess how reliable the findings were. In order to address the observed confounding factors, propensity score (PS) correction was first used. Second, using serum creatinine and the Modification of Diet in Renal Disease (MDRD) Study equation, we further corrected for estimated glomerular filtration rate (eGFR) (30), in models with several variables. Third, extreme BAA values (defined as less than the first or greater than the 99th percentiles) were not included. Fourth, conditional on the measured covariates, we computed an evaluation of possible residual confounding using E-values, which are the lowest degree of association on the OR scale that an unmeasured confounder would need to have with both the exposure and the outcome in order to completely explain the observed exposure-outcome association (31). Finally, we recalculated the biological ages with a modified set of biomarkers developed by Kwon and Belsky (24), including albumin, alkaline phosphate, total cholesterol, creatinine, glycated hemoglobin, systolic blood pressure, blood urea nitrogen, uric acid, lymphocyte, mean cell volume, and white blood cell count.
A multivariate linear regression model was used to investigate the relationships between BAA and IR surrogates. The relationship between IR and KS risk was examined using RCS and a multivariable-adjusted logistic regression model. Using the R ‘mediation’ package (version 4.5.0), we performed exploratory mediation analysis to assess the potential for statistical mediation by IR surrogates in the BAA-KS association.
R version 4.4.1 (The R Foundation for Statistical Computing, Vienna, Austria) was used for all statistical analyses. P values with two sides less than 0.05 were deemed statistically significant.
Results
Population characteristics
This cross-sectional analysis enrolled a cohort of 10,227 non-pregnant U.S. adults, with a mean age of 47.96 years and a nearly equal sex distribution (50.03% male), as detailed in Table 1. The population’s biological age was estimated at 42.62±17.51 years using the KDM-BA algorithm and 45.27±17.52 years using the PhenoAge algorithm. The mean values for the IR indices were as follows: TyG, 8.59±0.66; TyG-WC, 859.47±176.15 cm; TyG-WHtR, 5.13±1.05; TyG-BMI, 252.60±66.74 kg/m2; and METS-IR, 43.58±12.49 (see Table 1 and Table S2). As presented in Tables S3,S4, participants exhibiting higher KDM-BA acceleration were significantly more likely (all P<0.001) to be female and younger, to belong to other racial/ethnic groups, to have lower educational attainment and poorer income status, and to have higher serum uric acid levels. This group also demonstrated a significantly higher prevalence of hypertension, diabetes, and hyperuricemia, alongside elevated values across all measured IR indices (TyG, TyG-WC, TyG-WHtR, TyG-BMI, and METS-IR). Participants with higher PhensoAge acceleration were more likely to be male and younger, poorly educated, have worse income status, have higher serum UA levels, be current smokers, have a sedentary lifestyle, have hypertension, diabetes, hyperuricemia, and have higher values of TyG, TyG-WC, TyG-WHtR, TyG-BMI, and METS-IR (all P<0.01) (Table S4). Females, young people, other races/ethnicities, current smokers, current non-drinkers, and those with high school and below education attainment, low family PIR, sedentary lifestyle, hypertension, diabetes, and hyperuricemia had significantly higher KDM-BA accelerations (Table S5). Males, young people, other races/ethnicities, current smokers, current non-drinkers, and those with high school and below educational attainment, low family PIR, sedentary lifestyle, hypertension, diabetes, and hyperuricemia had significantly higher PhenoAge accelerations (Table S6).
Table 1. Characteristics of study participants.
| Characteristics | Study sample (N=10,227) |
|---|---|
| Age, years | 47.96±16.35 |
| UA, mg/dL | 5.51±1.4 |
| Male | 5,117 (50.03) |
| Race/ethnicity | |
| Non-Hispanic White | 4,390 (42.93) |
| Others | 5,837 (57.07) |
| Education attainment | |
| High school and below | 4,559 (44.58) |
| Above high school | 5,668 (55.42) |
| Family PIR | |
| <3.5 | 6,983 (68.28) |
| ≥3.5 | 3,244 (31.72) |
| Current smoking | 2,122 (20.75) |
| Current drinking | 7,268 (71.07) |
| Moderate-to-vigorous physical activity | 7,221 (70.61) |
| Hypertension | 4,106 (40.15) |
| Diabetes | 1,955 (19.12) |
| Hyperuricemia | 2,131 (20.84) |
| Biological ages | |
| KDM-BA, years | 42.62±17.51 |
| PhenoAge, years | 45.27±17.52 |
| KDM-BA acceleration | 0.00±1.00 |
| PhenoAge acceleration | 0.00±1.00 |
| Insulin resistance surrogates | |
| TyG | 8.59±0.66 |
| TyG-WC, cm | 859.47±176.15 |
| TyG-WHtR | 5.13±1.05 |
| TyG-BMI, kg/m2 | 252.60±66.74 |
| METS-IR | 43.58±12.49 |
Continuous variables are expressed as mean ± SD, and categorical variables are presented as numbers (percentages). KDM-BA, Klemera-Doubal method biological age; METS-IR, metabolic score for insulin resistance; PhenoAge, phenotypic age; PIR, poverty-income ratio; SD, standard deviation; TyG, triglyceride glucose index; TyG-BMI, triglyceride glucose-body mass index; TyG-WC, triglyceride glucose-waist circumference; TyG-WHtR, triglyceride glucose-waist-to-height ratio; UA, uric acid.
Associations of BAAs with KS
The absolute prevalence of KS rose with increasing biological age acceleration, from 8.95% in the lowest KDM-BA acceleration tertile to 10.79% in the highest, and more markedly from 6.72% to 12.56% across PhenoAge acceleration tertiles. For KDM-BA acceleration, the OR was 1.23 (95% CI: 1.05–1.45) in the crude model and 1.21 (95% CI: 1.02–1.44) after full adjustment. A stronger association was observed for PhenoAge acceleration, with a crude OR of 1.99 (95% CI: 1.69–2.36) and a multivariable-adjusted OR of 1.77 (95% CI: 1.47–2.13), all detailed in Table 2. Moreover, per 1-SD increment of PhenoAge acceleration was associated with 22% (OR: 1.22, 95% CI: 1.14–1.31) greater risk of KS. RCS analysis also illustrated positive associations of KDM-BA acceleration (Poverall=0.01, Pnonlinearity=0.02) and PhenoAge acceleration (Poverall<0.001, Pnonlinearity=0.053) with KS (Figure 1).
Table 2. Association between biological aging acceleration and risk of KS.
| Biological aging accelerations | Tertiles of biological aging acceleration measures | per 1-SD increment | ||
|---|---|---|---|---|
| Tertile 1 (lowest) | Tertile 2 (medium) | Tertile 3 (highest) | ||
| KDM-BA acceleration | ||||
| Range, years | <−0.43 | −0.43 to 0.32 | >0.32 | – |
| Median, years | −0.89 | −0.07 | 0.84 | – |
| Case/control (%) | 305/3,104 (8.95) | 301/3,108 (8.83) | 368/3,041 (10.79) | – |
| OR (95% CI) | ||||
| Crude model | 1.00 (reference) | 0.99 (0.83–1.16) | 1.23 (1.05–1.45) | 1.09 (1.03–1.17) |
| Model 1 | 1.00 (reference) | 1.18 (1.00–1.40) | 1.42 (1.21–1.68) | 1.13 (1.06–1.20) |
| Model 2 | 1.00 (reference) | 1.11 (0.93–1.32) | 1.21 (1.02–1.44) | 1.05 (0.98–1.12) |
| PhenoAge acceleration | ||||
| Range, years | <−0.49 | −0.49 to 0.30 | >0.30 | – |
| Median, years | −0.92 | −0.11 | 0.89 | – |
| Case/control (%) | 229/3,180 (6.72) | 317/3,092 (9.3) | 428/2,981 (12.56) | – |
| OR (95% CI) | ||||
| Crude model | 1.00 (reference) | 1.42 (1.19–1.70) | 1.99 (1.69–2.36) | 1.30 (1.22–1.38) |
| Model 1 | 1.00 (reference) | 1.47 (1.23–1.77) | 2.01 (1.69–2.39) | 1.29 (1.21–1.37) |
| Model 2 | 1.00 (reference) | 1.41 (1.17–1.69) | 1.77 (1.47–2.13) | 1.22 (1.14–1.31) |
Model 1 was adjusted for age (years, continuous), sex (male, female), and race/ethnicity (non-Hispanic White, others). Model 2 was further adjusted for educational attainment (high school and below, above high school), family poverty-income ratio (<3.5, ≥3.5), current smoking (yes, no), current drinking (yes, no), moderate-to-vigorous physical activity (yes, no), hypertension (yes, no), diabetes (yes, no), and hyperuricemia (yes, no). CI, confidence interval; KDM-BA, Klemera-Doubal method biological age; KS, kidney stone; OR, odds ratio; PhenoAge, phenotypic age.
Figure 1.
Correlations between the risk of KS and (A) KDM-BA and (B) phenoage acceleration. The shaded area denotes the 95% confidence interval, while the line indicates the multivariable-adjusted odds ratio. Age (years, continuous), sex (male, female), race/ethnicity (non-Hispanic White, others), education level (high school and below, above high school), family poverty-income ratio (<3.5, ≥3.5), current drinking and smoking (yes, no), moderate-to-vigorous physical activity (yes, no), hypertension (yes, no), diabetes (yes, no), and hyperuricemia (yes, no) were all taken into account when adjusting the models. CI, confidence interval; KDM-BA, Klemera-Doubal method biological age; KS, kidney stone; OR, odds ratio; PhenoAge, phenotypic age.
Stratified, interaction, and sensitivity analyses
Stratified and interaction analyses were performed to evaluate whether the associations of BAA with KS were modified by key demographic and clinical factors (Tables S7,S8). The positive association between PhenoAge acceleration and KS was stronger in female adults (Pinteraction=0.02). We also assessed the additive interaction at the additive scale (Tables S9,S10). Significant additive interactions between KDM-BA acceleration and sex, race, hypertension, and diabetes were observed (Table S9). Male, non-Hispanic white, hypertensive, diabetic subjects with KDM-BA accelerations >0 were faced with significantly higher risk of KS as compared with female, other race, non-hypertensive, non-diabetic subjects with KDM-BA accelerations ≤0 (Table S9). Moreover, age, sex, race/ethnicity, current smoking, sedentary lifestyle, hypertension, and diabetes had additive interactions with PhenoAge acceleration (Table S10). Old, male, non-Hispanic white, current smoking, physically inactive, hypertensive, diabetic subjects with KDM-BA accelerations >0 were faced with significantly higher risk of KS as compared with young, female, other race, current non-smoking, physically active, non-hypertensive, non-diabetic subjects with KDM-BA accelerations ≤0 (Table S10).
Multiple sensitivity analyses confirmed the robustness of the primary findings. PS-adjusted models maintained significant associations between biological age acceleration and KS, with ORs of 1.17 (95% CI: 1.00–1.38) for KDM-BA and 1.63 (95% CI: 1.38–1.94) for PhenoAge when comparing extreme tertiles (Table S11). These associations persisted after additional adjustment for eGFR and exclusion of outliers (Tables S12,S13). E-value analysis suggested considerable robustness to unmeasured confounding (KDM-BA: 1.71; PhenoAge: 3.50; Table S14). Furthermore, analyses using modified biomarker sets yielded consistent results, with multivariable-adjusted ORs of 1.23 (95% CI: 1.03–1.46) for KDM-BA and 1.30 (95% CI: 1.08–1.58) for PhenoAge acceleration (Table S15).
Mediation analyses
In fully adjusted models, all five IR indices showed significant, positive associations with both measures of BAA. Compared to the lowest tertile, the highest tertile of KDM-BA acceleration was associated with increased levels of TyG (β=0.21, 95% CI: 0.18–0.24), TyG-WC (β=68.46, 95% CI: 60.85–76.07), TyG-WHtR (β=0.40, 95% CI: 0.36–0.45), TyG-BMI (β=27.87, 95% CI: 24.85–30.90), and METS-IR (β=4.59, 95% CI: 4.02–5.16) (Table S16). Similarly, per 1-SD increment in PhenoAge acceleration was associated with increases in TyG (β=0.02, 95% CI: 0.01–0.04), TyG-WC (β=40.54, 95% CI: 37.43–43.64), TyG-WHtR (β=0.23, 95% CI: 0.21–0.25), TyG-BMI (β=16.86, 95% CI: 15.62–18.09), and METS-IR (β=3.31, 95% CI: 3.08–3.54) (Table S17).
Multivariable-adjusted analyses demonstrated significant, dose-response relationships between all five IR surrogates and KS risk, both when comparing extreme tertiles and per 1-SD increment (Table S18). RCS analyses confirmed these positive, linear associations (Figure 2). Exploratory mediation analysis suggested that the association between PhenoAge acceleration and KS could be partially explained statistically by TyG-WC, TyG-WHtR, TyG-BMI, and METS-IR, with mediated proportions ranging from 25.47% to 29.34% (all P<0.001), and a minor but significant mediation by TyG (1.67%, P=0.01) (Table 3). In contrast, no significant mediation by these IR surrogates was observed for the KDM-BA acceleration-KS association (Table S19).
Figure 2.
Relationships between the risk of KS and (A) TyG, (B) TyG-WC, (C) TyG-WHtR, (D) TyG-BMI, and (E) METS-IR. The shaded area denotes the 95% confidence interval, while the line indicates the multivariable-adjusted odds ratio. Age (years, continuous), sex (male, female), race/ethnicity (non-Hispanic White, others), education level (high school and below, above high school), family poverty-income ratio (<3.5, ≥3.5), current drinking and smoking (yes, no), moderate-to-vigorous physical activity (yes, no), hypertension (yes, no), diabetes (yes, no), and hyperuricemia (yes, no) were all taken into account when adjusting the models. CI, confidence interval; KS, kidney stone; METS-IR, metabolic score for insulin resistance; OR, odds ratio; TyG, triglyceride glucose index; TyG-BMI, triglyceride glucose-body mass index; TyG-WC, triglyceride glucose-waist circumference; TyG-WHtR, triglyceride glucose-waist height ratio.
Table 3. Proportions mediated by insulin resistance surrogates in the association between PhenoAge acceleration and risk of KS.
| Insulin resistance surrogates | ACME (95% CI) | ADE (95% CI) | Proportion mediated (95% CI) | P value |
|---|---|---|---|---|
| TyG | 0.000 (0.000–0.001) | 0.017 (0.011–0.024) | 1.67 (0.34–4.06) | 0.01 |
| TyG-WC | 0.005 (0.003–0.006) | 0.013 (0.006–0.019) | 26.37 (15.78–44.13) | <0.001 |
| TyG-WHtR | 0.005 (0.004–0.007) | 0.013 (0.006–0.019) | 28.44 (17.92–46.13) | <0.001 |
| TyG-BMI | 0.005 (0.003–0.006) | 0.013 (0.007–0.020) | 25.47 (15.49–42.11) | <0.001 |
| METS-IR | 0.005 (0.003–0.007) | 0.013 (0.006–0.019) | 29.34 (17.88–50.64) | <0.001 |
Age (years, continuous), gender, race/ethnicity (non-Hispanic White, others), educational attainment (high school and below, above high school), family poverty-income ratio (<3.5, ≥3.5), moderate-to-vigorous physical activity (yes, no), hypertension (yes, no), diabetes (yes, no), and hyperuricemia (yes, no) were all taken into account when creating the models. ACME, average causal mediation effect; ADE, average direct effect; CI, confidence interval; KS, kidney stone; METS-IR, metabolic score for insulin resistance; PhenoAge, phenotypic age; TyG, triglyceride glucose index; TyG-BMI, triglyceride glucose-body mass index; TyG-WC, triglyceride glucose-waist circumference; TyG-WHtR, triglyceride glucose-waist-to-height ratio.
Discussion
This study demonstrated that accelerated biological aging was associated with a significantly increased risk of KS. Participants in the highest KDM-BA and PhenoAge acceleration tertiles exhibited a 21% and 77% higher risk of KS, respectively. The mediation analysis suggested that IR surrogates, including TyG, TyG-WC, TyG-WHtR, TyG-BMI, and METS-IR, partially explained the relationship between PhenoAge acceleration and KS, with mediation proportions ranging from 1.67% to 29.34%. These findings may help inform future research into preventive strategies
The stronger association and clearer mediation observed for PhenoAge likely stem from fundamental differences in the biomarker composition of each algorithm. The KDM-BA measure used here primarily reflects cardiometabolic and organ function (via blood pressure, cholesterol, renal, and hepatic markers). In contrast, PhenoAge intrinsically incorporates markers of inflammation (CRP), immune cell distribution (lymphocyte percentage, white blood cell count), and red blood cell properties (mean cell volume, red cell distribution width), capturing a broader spectrum of immunological and cellular aging processes. Given the established role of inflammation and oxidative stress in KS pathogenesis, PhenoAge’s stronger association and the significant mediation through IR—a process tightly linked to inflammation—suggest that inflammatory aging may be a particularly salient pathway connecting biological aging to KS risk.
The association between accelerated biological aging and KS risk has not been explored in previous animal or clinical studies. Apart from traditional KS risk factors, such as diet, hydration, and metabolic disorders (32,33), our findings suggest that accelerated biological aging may be a risk factor for KS. Mechanisms of accelerated aging, such as mitochondrial dysfunction (15), oxidative stress (34,35), and chronic inflammation (6), are involved in KS pathogenesis (36-39). These age-related processes converge with the mechanisms of KS, suggesting a shared pathological basis. Recent studies have highlighted the critical roles of mitochondrial dysfunction, leucine-rich repeat containing protein 3 (NLRP3) inflammasome activation, endoplasmic reticulum stress, and cell death in the retention and aggregation of calcium oxalate (CaOx) crystals (38). Urine supersaturation may induce reactive oxygen species production and oxidative stress, which could trigger an inflammatory immune response that may promote Randall’s plaque formation and calcium stone development (40). Inflammation, involving immune cells, the NLRP3 inflammasome, and pro-inflammatory cytokines such as interleukin-1β and interleukin-18, MAY PLAY a critical role in tissue damage and stone formation in KS (41). This link between biological aging and KS highlights the need to consider aging in KS research and suggests that targeting aging-related mechanisms, such as improving mitochondrial function, managing oxidative stress, and reducing inflammation, may offer novel strategies for KS prevention and treatment. Biological aging is a gradually accumulating physiological process involving functional decline of various organ systems (12,13,42). As aging progresses, cells and tissues experience functional deterioration, metabolic dysregulation, heightened immune responses, and increased apoptosis (12,14).
Higher homeostasis model assessment of insulin resistance (HOMA-IR) values were positively associated with an increased risk of KS. IR and hyperglycemia may contribute to KS development, particularly in men (43). Compared with healthy matched controls, calcium stone formers without diabetes or other comorbidities also exhibited signs of IR, further supporting the potential role of IR in KS formation (22).
The pathophysiological mechanisms of stone formation in obese patients are primarily associated with IR, dietary factors, and lithogenic characteristics of urine. IR may lower urinary pH by altering renal acid-base metabolism, thereby increasing the risk of uric acid KS (44). Additionally, metabolic syndrome, centered on IR, is associated with more severe KS, characterized by increased urinary calcium, uric acid, and oxalate excretion along with decreased urinary citrate excretion (45). The study showed that higher levels of IR were significantly correlated with lower urinary citrate excretion, with patients exhibiting hypocitraturia demonstrating greater IR than those with normocitraturia, potentially due to alterations in citrate, sodium potassium, and hydrogen ion renal tubular transport functions induced by IR (46). Biological aging is associated with IR, which in turn is linked to a higher risk of KS through metabolic disturbances and changes in urinary composition. This suggests that the aging-IR-KS axis may represent a potential pathway worthy of further investigation. However, this mediating effect was not 100%, which suggests the presence of other potential mechanisms.
This study is the first to explore the association between BAA and KS and to examine whether IR may help explain this association. However, this study had several limitations. First, the cross-sectional nature of our data means the temporal sequence between BAA, IR, and KS cannot be established. Therefore, the observed statistical mediation should be interpreted as identifying a potential explanatory pathway consistent with a biologically plausible model (BAA → IR → KS), rather than as evidence of a causal chain. Second, reverse causation is plausible, as recurrent KS episodes or their underlying causes (e.g., chronic inflammation, metabolic dysregulation) could themselves accelerate biological aging processes, potentially inflating the observed associations. Furthermore, unmeasured common causes, such as detailed dietary patterns (e.g., sodium, oxalate, or animal protein intake), genetic predispositions, or environmental exposures, could confound the associations between BAA, IR, and KS. However, E-value analysis suggested that unmeasured confounding is unlikely to negate the observed associations. Third, KS diagnosis was based on a self-reported questionnaire item. This approach may underestimate the true prevalence of KS by missing asymptomatic cases and is vulnerable to recall inaccuracy. Further studies should employ longitudinal designs to establish causal relationships, use objective diagnostic methods to confirm KS cases, and have a better understanding of the mechanisms linking biological aging to KS. Fourth, the diagnosis of KS was based on self-report without information on stone composition. Given that uric acid stones are more prevalent in older individuals, those with diabetes, metabolic syndrome, and elevated serum uric acid levels—all factors closely related to IR—our findings may be particularly driven by uric acid stones. Consequently, the observed associations may not generalize to calcium stones, and the role of biological aging and IR in calcium stone formation remains unclear. Fifth, the use of complete‑case analysis resulted in the exclusion of approximately two‑thirds of the initially eligible sample, primarily due to missing biomarker or covariate data. This differential missingness could affect the generalizability of our findings. Sixth, our primary analyses did not incorporate NHANES sampling weights, which may affect the generalizability of point estimates and the precision of standard errors.
Conclusions
This study identified a significant association between accelerated biological aging and prevalent KS. Exploratory analysis suggested that IR may statistically account for part of this association. These results highlight the potential of targeting metabolic abnormalities and aging processes to prevent KS.
Supplementary
The article’s supplementary files as
Acknowledgments
The authors are grateful to the National Health and Nutrition Examination Survey (NHANES) for providing the data and to all participants for their contributions to the survey.
Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki.
Footnotes
Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://tau.amegroups.com/article/view/10.21037/tau-2025-741/rc
Funding: None.
Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tau.amegroups.com/article/view/10.21037/tau-2025-741/coif). The authors have no conflicts of interest to declare.
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